public FeatureSet(IEnumerable<RawDataModel> trainingModel, IEnumerable<RawDataModel> testModel, Parameters parameters, int[] size = null)
{
GroupSize = parameters.GroupSize;
SlideWindow = parameters.SlideWindow;
ValidationSetSize = parameters.ValidationSetSize;
TrainingModel = trainingModel;
TestModel = testModel;
VSizes = size ?? new[] { 4, 2, 4, 4, 2 };
Size = VSizes.Sum() * GroupSize;
}
private static void Main()
{
var parameters = new Parameters
{
Layers = (ConfigurationManager.AppSettings["LayersCount"] ?? "6,6,6,6,6").Split(',').Select(Int32.Parse).ToList(),
HasBias = bool.Parse(ConfigurationManager.AppSettings["HasBias"] ?? "true"),
IterationsCount = int.Parse(ConfigurationManager.AppSettings["IterationsCount"] ?? "2500"),
LearingCoefficient = double.Parse(ConfigurationManager.AppSettings["LearingCoefficient"] ?? "0.01", CultureInfo.InvariantCulture),
InertiaCoefficient = double.Parse(ConfigurationManager.AppSettings["InertiaCoefficient"] ?? "0.01", CultureInfo.InvariantCulture),
AcceptedError = double.Parse(ConfigurationManager.AppSettings["AcceptedError"] ?? "0.0000001", CultureInfo.InvariantCulture),
GroupSize = int.Parse(ConfigurationManager.AppSettings["GroupSize"] ?? "10"),
TimeWindow = int.Parse(ConfigurationManager.AppSettings["TimeWindow"] ?? "5"),
SlideWindow = int.Parse(ConfigurationManager.AppSettings["SlideWindow"] ?? "1"),
ValidationSetSize = double.Parse(ConfigurationManager.AppSettings["ValidationSetSize"] ?? "15", CultureInfo.InvariantCulture)
};
var trainingSetPath =
Path.Combine(ConfigurationManager.AppSettings["PathToTestFiles"] +
ConfigurationManager.AppSettings["TrainingSet"]);
var testSetPath =
Path.Combine(ConfigurationManager.AppSettings["PathToTestFiles"] +
ConfigurationManager.AppSettings["TestSet"]);
var parser = new Parser();
var classes = new Classification();
var trainingSetModel = parser.ReadFile(trainingSetPath);
var testSetModel = parser.ReadFile(testSetPath);
trainingSetModel = classes.AddClasses(trainingSetModel, parameters);
testSetModel = classes.AddClasses(testSetModel, parameters);
if ((ConfigurationManager.AppSettings["Type"] ?? "1") == "1")
{
var classification = new ProblemBase(parameters);
classification.Execute(trainingSetModel, testSetModel);
}
else
{
var svm = new SVM(parameters);
svm.Execute(trainingSetModel, testSetModel);
}
}
public ProblemBase(Parameters parameters)
{
Parameters = parameters;
Initialize();
}
public List<RawDataModel> AddClasses(List<RawDataModel> set, Parameters parameters)
{
var time = parameters.TimeWindow;
var indexFirst = set.FirstOrDefault(x => x.Status == "T") ?? set.First();
var indexLast = set[set.Count - 1];
var endTime = indexFirst.UpdateTime;
endTime = endTime.AddMinutes(time);
var counterSet = (indexLast.UpdateTime.Hour - indexFirst.UpdateTime.Hour)*60 +
(indexLast.UpdateTime.Minute - indexFirst.UpdateTime.Minute);
var counter = 1;
//zmienne dla sell
var sellMiddlePrice = new Double[(int) Math.Ceiling((double) counterSet/time) + 1];
var sellRecordCount = new int[(int) Math.Ceiling((double) counterSet/time) + 1];
var sellClassForTime = new int[(int) Math.Ceiling((double) counterSet/time)];
//zmienne dla buy
var buyMidlePrice = new Double[(int) Math.Ceiling((double) counterSet/time) + 1];
var buyRecordCount = new int[(int) Math.Ceiling((double) counterSet/time) + 1];
var buyClassForTime = new int[(int) Math.Ceiling((double) counterSet/time)];
foreach (var el in set)
{
if (el.Status == "1") //dane po nocnej zmianie
{
if (el.OrderType == 1)
{
//dla sell
sellMiddlePrice[0] += el.PricePoint;
sellRecordCount[0] += 1;
el.Group = 0;
}
else
{
//dla buy
buyMidlePrice[0] += el.PricePoint;
buyRecordCount[0] += 1;
el.Group = 0;
}
}
else
{
if (DateTime.Compare(el.UpdateTime, endTime) < 0)
{
if (el.OrderType == 1)
{
sellMiddlePrice[counter] += el.PricePoint;
sellRecordCount[counter] += 1;
el.Group = counter;
}
else
{
buyMidlePrice[counter] += el.PricePoint;
buyRecordCount[counter] += 1;
el.Group = counter;
}
}
else
{
if (el.OrderType == 1)
{
counter++;
endTime = endTime.AddMinutes(time);
sellMiddlePrice[counter] += el.PricePoint;
sellRecordCount[counter] += 1;
el.Group = counter;
}
else
{
counter++;
endTime = endTime.AddMinutes(time);
buyMidlePrice[counter] += el.PricePoint;
buyRecordCount[counter] += 1;
el.Group = counter;
}
}
}
}
//wyliczamy srednią dla każdej grupy
for (var i = 0; i < sellMiddlePrice.Length; i++)
{
if (sellRecordCount[i] != 0)
sellMiddlePrice[i] = sellMiddlePrice[i]/sellRecordCount[i];
else
sellMiddlePrice[i] = 0;
}
for (var i = 0; i < buyMidlePrice.Length; i++)
{
if (buyRecordCount[i] != 0)
buyMidlePrice[i] = buyMidlePrice[i]/buyRecordCount[i];
else
buyMidlePrice[i] = 0;
}
//przypisujemy klase na podstawie średniej
for (var i = 0; i < sellClassForTime.Length; i++)
{
if (sellMiddlePrice[i] < sellMiddlePrice[i + 1] && sellMiddlePrice[i + 1] != 0)
sellClassForTime[i] = 2; //wzrost
else if (sellMiddlePrice[i] > sellMiddlePrice[i + 1] && sellMiddlePrice[i + 1] != 0)
sellClassForTime[i] = 0; // spadek
else
sellClassForTime[i] = 1; //stały
}
for (var i = 0; i < buyClassForTime.Length; i++)
{
if (buyMidlePrice[i] < buyMidlePrice[i + 1] && buyMidlePrice[i + 1] != 0)
buyClassForTime[i] = 2; //wzrost
else if (buyMidlePrice[i] > buyMidlePrice[i + 1] && buyMidlePrice[i + 1] != 0)
buyClassForTime[i] = 0; // spadek
else
buyClassForTime[i] = 1; //stały
}
//Finalne przypisanie kalsy do rekordu
foreach (var el in set.Where(el => el.Group < sellClassForTime.Length))
if (el.OrderType == 1)
el.SellClass = sellClassForTime[el.Group];
else
el.BuyClass = buyClassForTime[el.Group];
return set;
}
public SVM(Parameters parameters) : base(parameters)
{
}
